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        "# code by Tae Hwan Jung(Jeff Jung) @graykode, Derek Miller @dmmiller612\n",
        "# Reference : https://github.com/jadore801120/attention-is-all-you-need-pytorch\n",
        "#           https://github.com/JayParks/transformer\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# S: Symbol that shows starting of decoding input\n",
        "# E: Symbol that shows starting of decoding output\n",
        "# P: Symbol that will fill in blank sequence if current batch data size is short than time steps\n",
        "\n",
        "def make_batch(sentences):\n",
        "    input_batch = [[src_vocab[n] for n in sentences[0].split()]]\n",
        "    output_batch = [[tgt_vocab[n] for n in sentences[1].split()]]\n",
        "    target_batch = [[tgt_vocab[n] for n in sentences[2].split()]]\n",
        "    return torch.LongTensor(input_batch), torch.LongTensor(output_batch), torch.LongTensor(target_batch)\n",
        "\n",
        "def get_sinusoid_encoding_table(n_position, d_model):\n",
        "    def cal_angle(position, hid_idx):\n",
        "        return position / np.power(10000, 2 * (hid_idx // 2) / d_model)\n",
        "    def get_posi_angle_vec(position):\n",
        "        return [cal_angle(position, hid_j) for hid_j in range(d_model)]\n",
        "\n",
        "    sinusoid_table = np.array([get_posi_angle_vec(pos_i) for pos_i in range(n_position)])\n",
        "    sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i\n",
        "    sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1\n",
        "    return torch.FloatTensor(sinusoid_table)\n",
        "\n",
        "def get_attn_pad_mask(seq_q, seq_k):\n",
        "    batch_size, len_q = seq_q.size()\n",
        "    batch_size, len_k = seq_k.size()\n",
        "    # eq(zero) is PAD token\n",
        "    pad_attn_mask = seq_k.data.eq(0).unsqueeze(1)  # batch_size x 1 x len_k(=len_q), one is masking\n",
        "    return pad_attn_mask.expand(batch_size, len_q, len_k)  # batch_size x len_q x len_k\n",
        "\n",
        "def get_attn_subsequent_mask(seq):\n",
        "    attn_shape = [seq.size(0), seq.size(1), seq.size(1)]\n",
        "    subsequent_mask = np.triu(np.ones(attn_shape), k=1)\n",
        "    subsequent_mask = torch.from_numpy(subsequent_mask).byte()\n",
        "    return subsequent_mask\n",
        "\n",
        "class ScaledDotProductAttention(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(ScaledDotProductAttention, self).__init__()\n",
        "\n",
        "    def forward(self, Q, K, V, attn_mask):\n",
        "        scores = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(d_k) # scores : [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)]\n",
        "        scores.masked_fill_(attn_mask, -1e9) # Fills elements of self tensor with value where mask is one.\n",
        "        attn = nn.Softmax(dim=-1)(scores)\n",
        "        context = torch.matmul(attn, V)\n",
        "        return context, attn\n",
        "\n",
        "class MultiHeadAttention(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MultiHeadAttention, self).__init__()\n",
        "        self.W_Q = nn.Linear(d_model, d_k * n_heads)\n",
        "        self.W_K = nn.Linear(d_model, d_k * n_heads)\n",
        "        self.W_V = nn.Linear(d_model, d_v * n_heads)\n",
        "        self.linear = nn.Linear(n_heads * d_v, d_model)\n",
        "        self.layer_norm = nn.LayerNorm(d_model)\n",
        "\n",
        "    def forward(self, Q, K, V, attn_mask):\n",
        "        # q: [batch_size x len_q x d_model], k: [batch_size x len_k x d_model], v: [batch_size x len_k x d_model]\n",
        "        residual, batch_size = Q, Q.size(0)\n",
        "        # (B, S, D) -proj-> (B, S, D) -split-> (B, S, H, W) -trans-> (B, H, S, W)\n",
        "        q_s = self.W_Q(Q).view(batch_size, -1, n_heads, d_k).transpose(1,2)  # q_s: [batch_size x n_heads x len_q x d_k]\n",
        "        k_s = self.W_K(K).view(batch_size, -1, n_heads, d_k).transpose(1,2)  # k_s: [batch_size x n_heads x len_k x d_k]\n",
        "        v_s = self.W_V(V).view(batch_size, -1, n_heads, d_v).transpose(1,2)  # v_s: [batch_size x n_heads x len_k x d_v]\n",
        "\n",
        "        attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1) # attn_mask : [batch_size x n_heads x len_q x len_k]\n",
        "\n",
        "        # context: [batch_size x n_heads x len_q x d_v], attn: [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)]\n",
        "        context, attn = ScaledDotProductAttention()(q_s, k_s, v_s, attn_mask)\n",
        "        context = context.transpose(1, 2).contiguous().view(batch_size, -1, n_heads * d_v) # context: [batch_size x len_q x n_heads * d_v]\n",
        "        output = self.linear(context)\n",
        "        return self.layer_norm(output + residual), attn # output: [batch_size x len_q x d_model]\n",
        "\n",
        "class PoswiseFeedForwardNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(PoswiseFeedForwardNet, self).__init__()\n",
        "        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)\n",
        "        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)\n",
        "        self.layer_norm = nn.LayerNorm(d_model)\n",
        "\n",
        "    def forward(self, inputs):\n",
        "        residual = inputs # inputs : [batch_size, len_q, d_model]\n",
        "        output = nn.ReLU()(self.conv1(inputs.transpose(1, 2)))\n",
        "        output = self.conv2(output).transpose(1, 2)\n",
        "        return self.layer_norm(output + residual)\n",
        "\n",
        "class EncoderLayer(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(EncoderLayer, self).__init__()\n",
        "        self.enc_self_attn = MultiHeadAttention()\n",
        "        self.pos_ffn = PoswiseFeedForwardNet()\n",
        "\n",
        "    def forward(self, enc_inputs, enc_self_attn_mask):\n",
        "        enc_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs, enc_self_attn_mask) # enc_inputs to same Q,K,V\n",
        "        enc_outputs = self.pos_ffn(enc_outputs) # enc_outputs: [batch_size x len_q x d_model]\n",
        "        return enc_outputs, attn\n",
        "\n",
        "class DecoderLayer(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(DecoderLayer, self).__init__()\n",
        "        self.dec_self_attn = MultiHeadAttention()\n",
        "        self.dec_enc_attn = MultiHeadAttention()\n",
        "        self.pos_ffn = PoswiseFeedForwardNet()\n",
        "\n",
        "    def forward(self, dec_inputs, enc_outputs, dec_self_attn_mask, dec_enc_attn_mask):\n",
        "        dec_outputs, dec_self_attn = self.dec_self_attn(dec_inputs, dec_inputs, dec_inputs, dec_self_attn_mask)\n",
        "        dec_outputs, dec_enc_attn = self.dec_enc_attn(dec_outputs, enc_outputs, enc_outputs, dec_enc_attn_mask)\n",
        "        dec_outputs = self.pos_ffn(dec_outputs)\n",
        "        return dec_outputs, dec_self_attn, dec_enc_attn\n",
        "\n",
        "class Encoder(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Encoder, self).__init__()\n",
        "        self.src_emb = nn.Embedding(src_vocab_size, d_model)\n",
        "        self.pos_emb = nn.Embedding.from_pretrained(get_sinusoid_encoding_table(src_len+1, d_model),freeze=True)\n",
        "        self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])\n",
        "\n",
        "    def forward(self, enc_inputs): # enc_inputs : [batch_size x source_len]\n",
        "        enc_outputs = self.src_emb(enc_inputs) + self.pos_emb(torch.LongTensor([[1,2,3,4,0]]))\n",
        "        enc_self_attn_mask = get_attn_pad_mask(enc_inputs, enc_inputs)\n",
        "        enc_self_attns = []\n",
        "        for layer in self.layers:\n",
        "            enc_outputs, enc_self_attn = layer(enc_outputs, enc_self_attn_mask)\n",
        "            enc_self_attns.append(enc_self_attn)\n",
        "        return enc_outputs, enc_self_attns\n",
        "\n",
        "class Decoder(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Decoder, self).__init__()\n",
        "        self.tgt_emb = nn.Embedding(tgt_vocab_size, d_model)\n",
        "        self.pos_emb = nn.Embedding.from_pretrained(get_sinusoid_encoding_table(tgt_len+1, d_model),freeze=True)\n",
        "        self.layers = nn.ModuleList([DecoderLayer() for _ in range(n_layers)])\n",
        "\n",
        "    def forward(self, dec_inputs, enc_inputs, enc_outputs): # dec_inputs : [batch_size x target_len]\n",
        "        dec_outputs = self.tgt_emb(dec_inputs) + self.pos_emb(torch.LongTensor([[5,1,2,3,4]]))\n",
        "        dec_self_attn_pad_mask = get_attn_pad_mask(dec_inputs, dec_inputs)\n",
        "        dec_self_attn_subsequent_mask = get_attn_subsequent_mask(dec_inputs)\n",
        "        dec_self_attn_mask = torch.gt((dec_self_attn_pad_mask + dec_self_attn_subsequent_mask), 0)\n",
        "\n",
        "        dec_enc_attn_mask = get_attn_pad_mask(dec_inputs, enc_inputs)\n",
        "\n",
        "        dec_self_attns, dec_enc_attns = [], []\n",
        "        for layer in self.layers:\n",
        "            dec_outputs, dec_self_attn, dec_enc_attn = layer(dec_outputs, enc_outputs, dec_self_attn_mask, dec_enc_attn_mask)\n",
        "            dec_self_attns.append(dec_self_attn)\n",
        "            dec_enc_attns.append(dec_enc_attn)\n",
        "        return dec_outputs, dec_self_attns, dec_enc_attns\n",
        "\n",
        "class Transformer(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Transformer, self).__init__()\n",
        "        self.encoder = Encoder()\n",
        "        self.decoder = Decoder()\n",
        "        self.projection = nn.Linear(d_model, tgt_vocab_size, bias=False)\n",
        "    def forward(self, enc_inputs, dec_inputs):\n",
        "        enc_outputs, enc_self_attns = self.encoder(enc_inputs)\n",
        "        dec_outputs, dec_self_attns, dec_enc_attns = self.decoder(dec_inputs, enc_inputs, enc_outputs)\n",
        "        dec_logits = self.projection(dec_outputs) # dec_logits : [batch_size x src_vocab_size x tgt_vocab_size]\n",
        "        return dec_logits.view(-1, dec_logits.size(-1)), enc_self_attns, dec_self_attns, dec_enc_attns\n",
        "\n",
        "def showgraph(attn):\n",
        "    attn = attn[-1].squeeze(0)[0]\n",
        "    attn = attn.squeeze(0).data.numpy()\n",
        "    fig = plt.figure(figsize=(n_heads, n_heads)) # [n_heads, n_heads]\n",
        "    ax = fig.add_subplot(1, 1, 1)\n",
        "    ax.matshow(attn, cmap='viridis')\n",
        "    ax.set_xticklabels(['']+sentences[0].split(), fontdict={'fontsize': 14}, rotation=90)\n",
        "    ax.set_yticklabels(['']+sentences[2].split(), fontdict={'fontsize': 14})\n",
        "    plt.show()\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    sentences = ['ich mochte ein bier P', 'S i want a beer', 'i want a beer E']\n",
        "\n",
        "    # Transformer Parameters\n",
        "    # Padding Should be Zero\n",
        "    src_vocab = {'P': 0, 'ich': 1, 'mochte': 2, 'ein': 3, 'bier': 4}\n",
        "    src_vocab_size = len(src_vocab)\n",
        "\n",
        "    tgt_vocab = {'P': 0, 'i': 1, 'want': 2, 'a': 3, 'beer': 4, 'S': 5, 'E': 6}\n",
        "    number_dict = {i: w for i, w in enumerate(tgt_vocab)}\n",
        "    tgt_vocab_size = len(tgt_vocab)\n",
        "\n",
        "    src_len = 5 # length of source\n",
        "    tgt_len = 5 # length of target\n",
        "\n",
        "    d_model = 512  # Embedding Size\n",
        "    d_ff = 2048  # FeedForward dimension\n",
        "    d_k = d_v = 64  # dimension of K(=Q), V\n",
        "    n_layers = 6  # number of Encoder of Decoder Layer\n",
        "    n_heads = 8  # number of heads in Multi-Head Attention\n",
        "\n",
        "    model = Transformer()\n",
        "\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "    enc_inputs, dec_inputs, target_batch = make_batch(sentences)\n",
        "\n",
        "    for epoch in range(20):\n",
        "        optimizer.zero_grad()\n",
        "        outputs, enc_self_attns, dec_self_attns, dec_enc_attns = model(enc_inputs, dec_inputs)\n",
        "        loss = criterion(outputs, target_batch.contiguous().view(-1))\n",
        "        print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    # Test\n",
        "    predict, _, _, _ = model(enc_inputs, dec_inputs)\n",
        "    predict = predict.data.max(1, keepdim=True)[1]\n",
        "    print(sentences[0], '->', [number_dict[n.item()] for n in predict.squeeze()])\n",
        "\n",
        "    print('first head of last state enc_self_attns')\n",
        "    showgraph(enc_self_attns)\n",
        "\n",
        "    print('first head of last state dec_self_attns')\n",
        "    showgraph(dec_self_attns)\n",
        "\n",
        "    print('first head of last state dec_enc_attns')\n",
        "    showgraph(dec_enc_attns)"
      ],
      "outputs": [],
      "execution_count": null
    }
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